Automatic adjustment of helmet parameters based on a category of play

ABSTRACT

Embodiments include method, systems and computer program products for automatic adjustment of helmet parameters based on a category of play. Aspects include monitoring a plurality of sensors in a helmet and determining the category of play for a user of the helmet based on data received from the plurality of sensors. Aspects further include automatically adjusting one or more parameters of the helmet based on the category of play.

RELATED APPLICATIONS

This application is related to: U.S. Application No. 14/709,575; Filed:May 12, 2015; U.S. Application Ser. No.: 14/709,572; Filed: May 12,2015; U.S. Application Serial No.: 14/709,570; Filed: May 12, 2015; U.S.Application Ser. No.: 14/709,563; Filed: May 12, 2015; U.S. ApplicationSer. No.: 14/709,568; Filed: May 12, 2015; U.S. Application Ser. No.:14/709,564; Filed: May 12, 2015; U.S. Application Ser. No.: 14/664,987;Filed Mar. 23, 2015; U.S. Application Ser. No.: 14/664,989; Filed: Mar.23, 2015; and U.S. Application Ser. No.: 14/664,991; Filed: Mar. 23,2015; the contents of each of which are herein incorporated by referencein their entirety.

BACKGROUND

The present disclosure relates to the adjustment of helmet parameters tomitigate the risk of brain injuries, and more specifically, to methods,systems and computer program products for the automatic adjustment ofhelmet parameters based on a category of play to mitigate the risk ofbrain injuries.

Generally speaking, safety is a primary concern for both users ofhelmets and manufacturers of helmets. Helmets are used by individualsthat participate in activities that have risk of head trauma, such asthe area of sports, biking, motorcycling, etc. While helmets havetraditionally been used to provide protection from blunt force trauma tothe head, an increased awareness of concussion causing forces hasmotivated a need for advances in helmet technology to provide increasedprotection against concussions. A concussion is a type of traumaticbrain injury that is caused by a blow to the head that shakes the braininside the skull due to linear or rotational accelerations. Recently,research has linked concussions to a range of health problems, fromdepression to Alzheimer's, along with a range of brain injuries. Unlikesevere traumatic brain injuries, which result in lesions or bleedinginside the brain and are detectable using standard medical imaging, aconcussion is often invisible in brain tissue, and therefore onlydetectable by means of a cognitive change, where that change ismeasurable by changes to brain tissue actions, either neurophysiologicalor through muscle actions caused by the brain and the muscles resultingeffects on the environment, for example, speech sounds.

Currently available helmets include a hard outer shell and internalpadding that is designed to mitigate the risk of brain injuries. Thesehelmets are designed to accommodate all types of impacts regardless ofthe probability of the occurrence of specific impacts during varioustypes of usage.

SUMMARY

In accordance with an embodiment, a method for the automatic adjustmentof helmet parameters based on a category of play includes monitoring aplurality of sensors in a helmet and determining the category of playfor a user of the helmet based on data received from the plurality ofsensors. Aspects further include automatically adjusting one or moreparameters of the helmet based on the category of play.

In accordance with another embodiment, an adjustable helmet formitigating the risk of brain injuries includes a processor and one ormore sensors, the processor configured for performing a method. Themethod includes monitoring a plurality of sensors in a helmet anddetermining the category of play for a user of the helmet based on datareceived from the one or more of sensors. Aspects further includeautomatically adjusting one or more parameters of the helmet based onthe category of play.

In accordance with a further embodiment, a computer program product forthe automatic adjustment of helmet parameters based on a category ofplay includes a non-transitory storage medium readable by a processingcircuit and storing instructions for execution by the processing circuitfor performing a method. The method includes monitoring a plurality ofsensors in a helmet and determining the category of play for a user ofthe helmet based on data received from the plurality of sensors. Aspectsfurther include automatically adjusting one or more parameters of thehelmet based on the category of play.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe invention are apparent from the following detailed description takenin conjunction with the accompanying drawings in which:

FIG. 1 is a block diagram illustrating one example of a processingsystem for practice of the teachings herein;

FIG. 2 is a block diagram illustrating an adjustable helmet inaccordance with an exemplary embodiment;

FIG. 3 is a flow diagram of a method for the automatic adjustment ofhelmet parameters based on a category of play in accordance with anexemplary embodiment;

FIG. 4 is a flow diagram of another method for automatic adjustment ofhelmet parameters based on a category of play in accordance with anexemplary embodiment; and

FIG. 5 is a block diagram illustrating a system for monitoringadjustable helmets in accordance with an exemplary embodiment.

DETAILED DESCRIPTION

In accordance with exemplary embodiments of the disclosure, methods,systems and computer program products for the automatic adjustment ofhelmet parameters based on a category of play are provided. In exemplaryembodiments, the helmets include one or more sensors and one or moreadjustable parameters, such as an adjustable chin strap or adjustableinternal or external padding. In exemplary embodiments, the sensors mayinclude one or more accelerometers, gyroscopes, or the like. In oneembodiment, the outputs of the sensors are provided to a processor thatmonitors one or more physical movements or actions of the user anddetermines a category of play of the user of the helmet. In otherembodiments, the category of play of the user may be provided to thehelmet by the user or by another source. In exemplary embodiments, theprocessor makes adjustments to a protection profile of the helmet basedon the category of play. The protection profile of the helmet mayinclude, but is not limited to, the tightness of the chin strap, thesize of one or more pads of the helmet, the stiffness of one or morepads of the helmet and the lateral mobility of one or more pads of thehelmet. In exemplary embodiments, the protection profile for eachcategory of play can be determined based on a probability of certaintypes of impacts and events occurring during each category of play.

Referring to FIG. 1, there is shown an embodiment of a processing system100 for implementing the teachings herein. In this embodiment, thesystem 100 has one or more central processing units (processors) 101 a,101 b, 101 c, etc. (collectively or generically referred to asprocessor(s) 101). In one embodiment, each processor 101 may include areduced instruction set computer (RISC) microprocessor. Processors 101are coupled to system memory 114 and various other components via asystem bus 113. Read only memory (ROM) 102 is coupled to the system bus113 and may include a basic input/output system (BIOS), which controlscertain basic functions of system 100.

FIG. 1 further depicts an input/output (I/O) adapter 107 and a networkadapter 106 coupled to the system bus 113. I/O adapter 107 may be asmall computer system interface (SCSI) adapter that communicates with ahard disk 103 and/or tape storage drive 105 or any other similarcomponent. I/O adapter 107, hard disk 103, and tape storage device 105are collectively referred to herein as mass storage 104. Operatingsystem 120 for execution on the processing system 100 may be stored inmass storage 104. A network adapter 106 interconnects bus 113 with anoutside network 116 enabling data processing system 100 to communicatewith other such systems. A screen (e.g., a display monitor) 115 isconnected to system bus 113 by display adaptor 112, which may include agraphics adapter to improve the performance of graphics intensiveapplications and a video controller. In one embodiment, adapters 107,106, and 112 may be connected to one or more I/O busses that areconnected to system bus 113 via an intermediate bus bridge (not shown).Suitable I/O buses for connecting peripheral devices such as hard diskcontrollers, network adapters, and graphics adapters typically includecommon protocols, such as the Peripheral Component Interconnect (PCI).Additional input/output devices are shown as connected to system bus 113via user interface adapter 108 and display adapter 112. A keyboard 109,mouse 110, and speaker 111 all interconnected to bus 113 via userinterface adapter 108, which may include, for example, a Super I/O chipintegrating multiple device adapters into a single integrated circuit.

Thus, as configured in FIG. 1, the system 100 includes processingcapability in the form of processors 101, storage capability includingsystem memory 114 and mass storage 104, input means such as keyboard 109and mouse 110, and output capability including speaker 111 and display115. In one embodiment, a portion of system memory 114 and mass storage104 collectively store an operating system such as the AIX® operatingsystem from IBM Corporation to coordinate the functions of the variouscomponents shown in FIG. 1.

Referring to FIG. 2, a block diagram illustrating an adjustable helmet200 in accordance with an exemplary embodiment is shown. The term“helmet” may include, but is not intended to be limited to, a footballhelmet, a motorcycle helmet or the like. In exemplary embodiments, theadjustable helmet 200 includes one or more of the following: anaccelerometer 202, a chin strap 204, a padding 206, a gyroscope 208, aprocessor 210, a transceiver 212, a power supply 214 and a memory 216.In exemplary embodiments, the power supply 214 may be a batteryconfigured to provide power to one or more of the accelerometer 202, thegyroscope 208, the processor 210 and the transceiver 212.

In one embodiment, the processor 210 is configured to receive an outputfrom one or more of the accelerometer 202 and the gyroscope 208 and todetermine a category of play of the user of the adjustable helmet 200.In other embodiments, the processor 210 may be provided with thecategory of play of the user of the adjustable helmet 200 or the helmetmay receive the category of play from an external processing system viathe transceiver 212. As used herein, the term category of play means themanner in which the user is using the helmet. In one example, for afootball helmet, the category of play may refer to the position beingplayed by the user, running back, wide receiver, linemen, etc., or bythe current activity being performed by the user, running, blocking,jumping, etc.

In exemplary embodiments, the padding 206 of the adjustable helmet 200may include either or both of internal padding or external padding thatcan have one or more adjustable parameters. In one embodiment, thepadding 206 may include electroactive polymers that can be used tochange the size, shape, and/or stiffness of the padding 206. In anotherembodiment, the padding 206 may include inflatable padding that can beinflated and deflated by the adjustable helmet 200. In additionalembodiments, the padding 206 may be coupled to the helmet 200 by a linerthat can be adjusted to selectively allow the padding 206 to movelaterally in relation to the shell of the helmet. For example, the linermay be configured to allow the padding to slide, or slip, along thesurface of the shell of the helmet to reduce the torque on the user'shead during an impact. In exemplary embodiments, the degree, or amountof lateral movement, of the liner with respect to the shell may beautomatically adjusted based on the category of play of the user of thehelmet.

Referring now to FIG. 3, a flow diagram of a method 300 for theautomatic adjustment of helmet parameters based on a category of play inaccordance with an exemplary embodiment is shown. As shown at block 302,the method 300 includes monitoring a plurality of sensors in anadjustable helmet. In exemplary embodiments, the plurality of sensorsincludes one or more of an accelerometer and a gyroscope. Next, as shownat block 304, the method 300 includes determining a category of play fora user of the helmet based on data received from the plurality ofsensors. In exemplary embodiments, the processor of the adjustablehelmet may create a baseline profile of the user during each category ofplay based on input form the accelerometer and the gyroscope and maystore the baseline profile in the memory. The processor may compare thereadings from the accelerometer and the gyroscope with the storedbaseline profile to determine the category of play for a user.

Continuing with reference to FIG. 3, as shown at block 306, the method300 includes automatically adjusting one or more parameters of thehelmet based on the category of play. In exemplary embodiments, theadjustments may include, but are not limited to, adjusting the stiffnesson the padding, adjusting the tightness of the chin strap, adjusting thesize of the padding, etc. The type and amount of an adjustment is chosenbased on a model of expected risks given certain plays to certain typesof hits and accelerations to mitigate the brain injury from these hitsand accelerations. The adjustments to the padding may be uniform ornon-uniform, i.e., the stiffness of all of the padding may not be thesame. For example, based on the category of play the stiffness of thepadding in the front portion of the helmet may be greater or less thatthe stiffness in the back of the helmet.

Optionally, the method 300 may include storing the determined states ofplay and adjustments made to the one or more parameters in a memory, asshown in block 308. In addition, the method 300 may also includetransmitting the data received from the plurality of sensors, thedetermined categories of play and adjustments made to the one or moreparameters to a processing system, as shown at block 310.

Referring now to FIG. 4, a flow diagram of another method 400 for theautomatic adjustment of helmet parameters based on a category of play inaccordance with an exemplary embodiment is shown. As shown at block 402,the method 400 includes receiving a category of play for a user of ahelmet. For example, the helmet may receive an input from the user thatindicates the category of play or the helmet may receive input from aseparate processing system via a transmitter that indicates the categoryof play. Next, as shown at decision block 404, the method 400 includesautomatically adjusting one or more parameters of the helmet based onthe category of play. In exemplary embodiments, the adjustments mayinclude, but are not limited to, adjusting the stiffness on the padding,adjusting the tightness of the chin strap, adjusting the size of thepadding, etc. The type and amount of an adjustment is chosen based on amodel of expected risks given certain plays to certain types of hits andaccelerations to mitigate the brain injury from these hits andaccelerations. The adjustments to the padding may be uniform ornon-uniform, i.e., the stiffness of all of the padding may not be thesame. For example, based on the category of play the stiffness of thepadding in the front portion of the helmet may be greater or less thatthe stiffness in the back of the helmet. Optionally, the method 400 mayinclude transmitting data received from plurality of sensors disposed inthe helmet, the category of play and adjustments made to the one or moreparameters to a processing system, as shown in block 406.

In exemplary embodiments, the processor of the helmet may use arisk/probability function that is constructed based on the category ofplay a wearer is engaged in to determine the parameters for theadjustable helmet for certain hits and certain angles of acceleration.In one embodiment, such as that shown in FIG. 4, the risk/probabilityfunction may receive a direct input of category of play, e.g. from aquarterback calling a certain play, or from a motorcycle signaling itsspeed, turn frequency, aggressiveness of the rider, ice on road, etc. Inone embodiment, such as that shown in FIG. 3, the risk/probabilityfunction may include making a determination of the category of playbased on data received from one or more sensors disposed in the helmet.

Referring now to FIG. 5, a block diagram illustrating a system 500 formonitoring adjustable helmets in accordance with an exemplary embodimentis shown. As illustrated the system 500 includes one or more adjustablehelmets 502, such as the one shown and described above with reference toFIG. 2, and a processing system 504, such as the one shown and describedabove with reference to FIG. 1. The processing system 504 is configuredto communicate with the helmets 502 and is also configured to store themedical history 506 of the users of the helmets 502. In exemplaryembodiments, the medical history 506 of the users of the helmets 502 maybe used by the helmet in determining what adjustments to make to thehelmet during play. In addition, the processing system 504 may include avirtual world display 508 that is configured to provide a display areal-time status of each of the users of the helmets. In exemplaryembodiments, the status may include the category of play of each user,any indications that the user may have suffered a traumatic braininjury, a duration of play of the user, a duration that the user hasbeen in the current category of play, or the like.

In exemplary embodiments, the user's history of collision or medicalconcerns may be used to determine a traumatic brain injury riskassessment, either by the embedded processor or the separate processingsystem. In addition, the helmet may be configured to provide a real-timefeed of the user's cognitive state to increase the confidence level ofthe need for a particular alert or indication. In exemplary embodiments,an aggregate indication may be used to summarize an overall state of agroup of players. This may also help to potentially identify area ofrisk in the dynamics of player-player interaction, overly aggressiveplayers, playing field conditions, etc. In exemplary embodiments, anautomatic feed from a user's history of collision or medical concernsmay also be provided to a processor of the helmet in order to update animpact risk model for each category of play. In addition, the processingsystem 504 may receive a real-time feed of the user's cognitive state,which can be used to update the risk models used by the helmets. Therisk models may also be sent to the virtual world display 508 of thegame and players, which allows the sports staff health professionals tovisualize the nature of potential problems.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A computer program product for automaticadjustment of helmet parameters based on a category of play, thecomputer program product comprising: a non-transitory storage mediumreadable by a processing circuit and storing instructions for executionby the processing circuit for performing a method comprising: monitoringa plurality of sensors in a helmet; determining the category of play fora user of the helmet based on data received from the plurality ofsensors; and automatically adjusting parameters of the helmet based onthe category of play, wherein the parameters include at least one of astiffness, a size and a shape of a padding of the helmet, wherein a typeof an adjustment and an amount of the adjustment are determined based ona model of expected risks for the category of play for the user.
 2. Thecomputer program product of claim 1, wherein the padding is disposed onan internal surface of the helmet.
 3. The computer program product ofclaim 1, the stiffness of the padding on the helmet includes increasingthe stiffness in a first area of the padding and decreasing thestiffness in a second area of the padding.
 4. The computer programproduct of claim 1, wherein automatically adjusting one or moreparameters of the helmet based on the category of play includesadjusting a tightness of a chin strap of the helmet.
 5. The computerprogram product of claim 4, wherein the one or more parameters areadjusted based on a risk profile associated with the category of play.6. The computer program product of claim 5, wherein the risk profile isfurther based on a medical history of the user of the helmet.
 7. Anadjustable helmet for mitigating the risk of brain injuries, comprising:a processor and one or more sensors, the processor configured to:monitoring the one or more sensors; determine a category of play for auser of the helmet based on data received from the plurality of sensors;and automatically adjust parameters of the helmet based on the categoryof play, wherein the parameters include at least one of a stiffness, asize and a shape of a padding of the helmet, wherein a type of anadjustment and an amount of the adjustment are determined based on amodel of expected risks for the category of play for the user.
 8. Thehelmet of claim 7, wherein the padding is disposed on an internalsurface of the helmet.
 9. The helmet of claim 7, the stiffness of thepadding on the helmet includes increasing the stiffness in a first areaof the padding and decreasing the stiffness in a second area of thepadding.
 10. The helmet of claim 7, wherein the one or more parametersare adjusted based on a risk profile associated with the category ofplay.
 11. The helmet of claim 10, wherein the risk profile is furtherbased on a medical history of the user of the helmet.